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Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach
OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the sp...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683916/ https://www.ncbi.nlm.nih.gov/pubmed/31144552 http://dx.doi.org/10.1177/0300060519850734 |
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author | Zhao, Youlin Ge, Liang Liu, Junwei Liu, Honghui Yu, Lei Wang, Ning Zhou, Yijun Ding, Xu |
author_facet | Zhao, Youlin Ge, Liang Liu, Junwei Liu, Honghui Yu, Lei Wang, Ning Zhou, Yijun Ding, Xu |
author_sort | Zhao, Youlin |
collection | PubMed |
description | OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. METHODS: We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. RESULTS: In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. CONCLUSION: The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns. |
format | Online Article Text |
id | pubmed-6683916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-66839162019-08-19 Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach Zhao, Youlin Ge, Liang Liu, Junwei Liu, Honghui Yu, Lei Wang, Ning Zhou, Yijun Ding, Xu J Int Med Res Case Report and Case Series OBJECTIVE: Hemorrhagic fever with renal syndrome (HFRS), a natural–focal infectious disease caused by hantaviruses, resulted in 37 deaths between 2011 and 2015 in Hubei Province, China. HFRS outbreaks are seasonally distributed, exhibiting heterogeneity in space and time. We aimed to identify the spatial and temporal characteristics of HFRS epidemics and their probable influencing factors. METHODS: We used the space–time cube (STC) method to investigate HFRS epidemics in different space–time locations. STC can be used to visualize the trajectories of moving objects (or changing tendencies) in space and time in three dimensions. We applied space–time statistical methods, including space–time hot spot and space–time local outlier analyses, based on a calculated STC model of HFRS cases, to identify spatial and temporal hotspots and outlier distributions. We used the space–time gravity center method to reveal associations between possible factors and HFRS epidemics. RESULTS: In this research, HFRS cases for each space–time location were defined by the STC model, which can present the dynamic characteristics of HFRS epidemics. The STC model delivered accurate and detailed results for the spatiotemporal patterns of HFRS epidemics. CONCLUSION: The methods in this paper can potentially be applied for infectious diseases with similar spatial and temporal patterns. SAGE Publications 2019-05-30 2019-07 /pmc/articles/PMC6683916/ /pubmed/31144552 http://dx.doi.org/10.1177/0300060519850734 Text en © The Author(s) 2019 http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Case Report and Case Series Zhao, Youlin Ge, Liang Liu, Junwei Liu, Honghui Yu, Lei Wang, Ning Zhou, Yijun Ding, Xu Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title | Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title_full | Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title_fullStr | Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title_full_unstemmed | Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title_short | Analyzing hemorrhagic fever with renal syndrome in Hubei Province, China: a space–time cube-based approach |
title_sort | analyzing hemorrhagic fever with renal syndrome in hubei province, china: a space–time cube-based approach |
topic | Case Report and Case Series |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6683916/ https://www.ncbi.nlm.nih.gov/pubmed/31144552 http://dx.doi.org/10.1177/0300060519850734 |
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